Reconstructing prevalence dynamics of wildlife pathogens from pooled and individual samples
Pathogen transmission studies require sample collection over extended periods, which can be challenging and costly, especially in the case of wildlife. A useful strategy can be to collect pooled samples, but this presents challenges when the goal is to estimate prevalence. This is because pooling ca...
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2024-08-01
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| Online Access: | https://peercommunityjournal.org/articles/10.24072/pcjournal.455/ |
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| author | Borremans, Benny Falvo, Caylee A Crowley, Daniel E Hoegh, Andrew Lloyd-Smith, James O Peel, Alison J Restif, Olivier Ruiz-Aravena, Manuel Plowright, Raina K |
| author_facet | Borremans, Benny Falvo, Caylee A Crowley, Daniel E Hoegh, Andrew Lloyd-Smith, James O Peel, Alison J Restif, Olivier Ruiz-Aravena, Manuel Plowright, Raina K |
| author_sort | Borremans, Benny |
| collection | DOAJ |
| description | Pathogen transmission studies require sample collection over extended periods, which can be challenging and costly, especially in the case of wildlife. A useful strategy can be to collect pooled samples, but this presents challenges when the goal is to estimate prevalence. This is because pooling can introduce a dilution effect where pathogen concentration is lowered by the inclusion of negative or lower-concentration samples, while at the same time a pooled sample can test positive even when some of the contributing samples are negative. If these biases are taken into account, the concentration of a pooled sample can be leveraged to infer the most likely proportion of positive individuals, and thus improve overall prevalence reconstruction, but few methods exist that account for the sample mixing process. We present a Bayesian multilevel model that estimates prevalence dynamics over time using pooled and individual samples in a wildlife setting. The model explicitly accounts for the complete mixing process that determines pooled sample concentration, thus enabling accurate prevalence estimation even from pooled samples only. As it is challenging to link individual-level metrics such as age, sex, or immune markers to infection status when using pooled samples, the model also allows the incorporation of individual-level samples. Crucially, when individual samples can test false negative, a potentially strong bias is introduced that results in incorrect estimates of regression coefficients. The model, however, can account for this by leveraging the combination of pooled and individual samples. Last, the model enables estimation of extrinsic environmental effects on prevalence dynamics. Using a simulated dataset inspired by virus transmission in flying foxes, we show that the model is able to accurately estimate prevalence dynamics, false negative rate, and covariate effects. We test model performance for a range of realistic sampling scenarios and find that while it is generally robust, there are a number of factors that should be considered in order to maximize performance. The model presents an important advance in the use of pooled samples for estimating prevalence dynamics in a wildlife setting, can be used with any biomarker of infection (Ct values, antibody levels, other infection biomarkers) and can be applied to a wide range of host-pathogen systems. |
| format | Article |
| id | doaj-art-0d1dc9fc98a44ee88f78d603c082651f |
| institution | Kabale University |
| issn | 2804-3871 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Peer Community In |
| record_format | Article |
| series | Peer Community Journal |
| spelling | doaj-art-0d1dc9fc98a44ee88f78d603c082651f2025-02-07T10:17:18ZengPeer Community InPeer Community Journal2804-38712024-08-01410.24072/pcjournal.45510.24072/pcjournal.455Reconstructing prevalence dynamics of wildlife pathogens from pooled and individual samples Borremans, Benny0https://orcid.org/0000-0002-7779-4107Falvo, Caylee A1Crowley, Daniel E2Hoegh, Andrew3Lloyd-Smith, James O4Peel, Alison J5Restif, Olivier6Ruiz-Aravena, Manuel7Plowright, Raina K8Wildlife Health Ecology Research Organization, San Diego, USA; Department of Public and Ecosystem Health, College of Veterinary Medicine, Cornell University, Ithaca, USA; Evolutionary Ecology Group, University of Antwerp, Antwerp, BelgiumDepartment of Public and Ecosystem Health, College of Veterinary Medicine, Cornell University, Ithaca, USADepartment of Public and Ecosystem Health, College of Veterinary Medicine, Cornell University, Ithaca, USADepartment of Mathematical Sciences, Montana State University, Bozeman, USADepartment of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, USACentre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Queensland, AustraliaDepartment of Veterinary Medicine, University of Cambridge, Cambridge, United KingdomDepartment of Public and Ecosystem Health, College of Veterinary Medicine, Cornell University, Ithaca, USA; Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Queensland, Australia; Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, Mississippi State, USADepartment of Public and Ecosystem Health, College of Veterinary Medicine, Cornell University, Ithaca, USAPathogen transmission studies require sample collection over extended periods, which can be challenging and costly, especially in the case of wildlife. A useful strategy can be to collect pooled samples, but this presents challenges when the goal is to estimate prevalence. This is because pooling can introduce a dilution effect where pathogen concentration is lowered by the inclusion of negative or lower-concentration samples, while at the same time a pooled sample can test positive even when some of the contributing samples are negative. If these biases are taken into account, the concentration of a pooled sample can be leveraged to infer the most likely proportion of positive individuals, and thus improve overall prevalence reconstruction, but few methods exist that account for the sample mixing process. We present a Bayesian multilevel model that estimates prevalence dynamics over time using pooled and individual samples in a wildlife setting. The model explicitly accounts for the complete mixing process that determines pooled sample concentration, thus enabling accurate prevalence estimation even from pooled samples only. As it is challenging to link individual-level metrics such as age, sex, or immune markers to infection status when using pooled samples, the model also allows the incorporation of individual-level samples. Crucially, when individual samples can test false negative, a potentially strong bias is introduced that results in incorrect estimates of regression coefficients. The model, however, can account for this by leveraging the combination of pooled and individual samples. Last, the model enables estimation of extrinsic environmental effects on prevalence dynamics. Using a simulated dataset inspired by virus transmission in flying foxes, we show that the model is able to accurately estimate prevalence dynamics, false negative rate, and covariate effects. We test model performance for a range of realistic sampling scenarios and find that while it is generally robust, there are a number of factors that should be considered in order to maximize performance. The model presents an important advance in the use of pooled samples for estimating prevalence dynamics in a wildlife setting, can be used with any biomarker of infection (Ct values, antibody levels, other infection biomarkers) and can be applied to a wide range of host-pathogen systems.https://peercommunityjournal.org/articles/10.24072/pcjournal.455/bat virus shedding, disease ecology, prevalence modeling, sample pooling, bayesian multilevel model, combinatorics |
| spellingShingle | Borremans, Benny Falvo, Caylee A Crowley, Daniel E Hoegh, Andrew Lloyd-Smith, James O Peel, Alison J Restif, Olivier Ruiz-Aravena, Manuel Plowright, Raina K Reconstructing prevalence dynamics of wildlife pathogens from pooled and individual samples Peer Community Journal bat virus shedding, disease ecology, prevalence modeling, sample pooling, bayesian multilevel model, combinatorics |
| title | Reconstructing prevalence dynamics of wildlife pathogens from pooled and individual samples
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| title_full | Reconstructing prevalence dynamics of wildlife pathogens from pooled and individual samples
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| title_fullStr | Reconstructing prevalence dynamics of wildlife pathogens from pooled and individual samples
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| title_full_unstemmed | Reconstructing prevalence dynamics of wildlife pathogens from pooled and individual samples
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| title_short | Reconstructing prevalence dynamics of wildlife pathogens from pooled and individual samples
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| title_sort | reconstructing prevalence dynamics of wildlife pathogens from pooled and individual samples |
| topic | bat virus shedding, disease ecology, prevalence modeling, sample pooling, bayesian multilevel model, combinatorics |
| url | https://peercommunityjournal.org/articles/10.24072/pcjournal.455/ |
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